A histogram displays numerical data by grouping data into "bins" of equal width. More...

Variables
	mu

	sigma

	x = mu + sigma * np.random.randn(10000)
	Holds a sample (or samples) from the “standard normal” distribution. More...

int	n = 0
	Number of counts in each bin of the histogram. More...

	bins = None
	Left hand edge of each bin. More...

	patches = None
	The individual patches used to create the histogram, e.g a collection of rectangles. More...

	density

	True

	facecolor

	alpha

	y = norm.pdf(bins, mu, sigma)

	l = plt.plot(bins, y, 'r--', linewidth=1)

Detailed Description

A histogram displays numerical data by grouping data into "bins" of equal width.

Each bin is plotted as a bar whose height corresponds to how many data points are in that bin.

Author: Paulo Roma

Since: 29/10/2016

See also: http://matplotlib.org/1.2.1/examples/pylab_examples/histogram_demo.html; https://numpy.org/doc/stable/reference/random/generated/numpy.random.randn.html; https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.hist.html; https://www.khanacademy.org/math/statistics-probability/displaying-describing-data/quantitative-data-graphs/a/histograms-review; https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html

Variable Documentation

◆ alpha

_04_histogram_demo.alpha

◆ bins

_04_histogram_demo.bins = None

Left hand edge of each bin.

◆ density

_04_histogram_demo.density

◆ facecolor

_04_histogram_demo.facecolor

◆ l

_04_histogram_demo.l = plt.plot(bins, y, 'r--', linewidth=1)

◆ mu

_04_histogram_demo.mu

◆ n

_04_histogram_demo.n = 0

Number of counts in each bin of the histogram.

◆ patches

_04_histogram_demo.patches = None

The individual patches used to create the histogram, e.g a collection of rectangles.

◆ sigma

_04_histogram_demo.sigma

◆ True

_04_histogram_demo.True

◆ x

_04_histogram_demo.x = mu + sigma * np.random.randn(10000)

Holds a sample (or samples) from the “standard normal” distribution.

◆ y

_04_histogram_demo.y = norm.pdf(bins, mu, sigma)

Variables